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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Zheve, Leon T. Gitau, Michael N. Masike, Lebogang University of Pretoria Dept. of Electrical Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331535162

The penetration of renewable energy systems has helped trigger the shift to DC distribution systems. Switching converters are the building blocks of DC distribution systems and these individual blocks have their unique control. Manufacturers of these building blocks tend to overlook the effect of coupling converters together that have closed loop control, and integration engineers face the challenge of voltage instability during integration. Load converters operating with closed-loop control behave as constant power loads and have a destabilizing negative incremental resistance that diminishes the damping of the system at the DC bus they are connected. The mode of control also influences how far the action of constant power loads drives the system into instability. This work compares the average current mode control and voltage mode control applied to small DC network to see which control mode is better to implement for DC distribution grids. Simulations of a small single bus DC distribution system comprising of two constant power loads and a source converter were carried out with the objective of comparing the extent of bus voltage deterioration when using either control modes. The simulation results show that for the same system average current mode control is better at maintaining voltage stability compared voltage mode control. © 2025 IEEE.

关键词： DC distribution systems

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Real-Time Dynamic Image Stitching Using GNN-Based Feature Matching 11

Real-Time Dynamic Image Stitching Using GNN-Based Feature Ma...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Suh, Young Hoon Kim, Eun Ho Choi, Yeong Gwang Jeon, JaeWook Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331509293

Especially in static situations, image stitching has been routinely used to seamlessly merge many photographs into a panorama. Using it in dynamic and real-time environments, however, presents a number of difficulties, such as managing shifting objects, changing surroundings, and processing limitations. An image stitching look at that works well in dynamic real-time situations is presented in this paper. The method makes use of GNN-based feature matching, dynamic object removal, and real-time synthesis techniques. Comparative studies suggest that, in comparison to traditional methods like SIFT and ORB, the suggested approach meets real-time processing requirements while achieving greater registration precision, lower homography RMSE, and consistent panoramic outputs. The findings imply that the algorithm might be used in fields where precision and efficiency are required in dynamic circumstances, like parking management systems, sports broadcasting, and autonomous driving. © 2025 IEEE.

关键词： Photointerpretation

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Structured Implementation of ML Algorithms for Cardiovascular Disease Detection 5

Structured Implementation of ML Algorithms for Cardiovascula...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Arora, Drishti Asif, Farhan Amity University Dept. of Computer Science & Engineering Uttar Pradesh India

ISBN: (纸本)9798350383522

Cardiovascular diseases encompass a range of medical conditions that impact the heart and blood vessels The prediction of heart disease pose considerable complexity and present a formidable challenge within the medical field According to the WHO, heart diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year, accounting for around 31% of all global deaths To address the need for accurate heart disease prediction, extensive research has employed advanced machine learning models This study conducted a comprehensive evaluation of seven distinct classification models, combining various physiological factors with well-known machine learning algorithms The model employed in this study includes Naïve Bayes, Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, and Voting Classifiers Using these models, a robust heart disease prediction system is designed, facilitating precise evaluation of an individual's risk The observation shows that through meticulous evaluation and comparison, the Random Forest algorithm, which is a bagging technique, outperforms the existing state-of-the-art methods It exhibited remarkable accuracy, yielding prediction results of approximately 90.16% This exceptional accuracy establishes the random forest algorithm as the pre-eminent model for precise and reliable heart disease prediction within the scope of this study. © 2024 IEEE.

关键词： Cardiovascular Disease Machine Learning Classifiers Performance Evaluation Predictive Analysis

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A Review on Deep Learning Based Question Answering with Natural Language Processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Javeed, Abdul Saleem Gopala Krishnan, C. Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350359688

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, Natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics. © 2024 IEEE.

关键词： and Medical Data Deep Learning Healthcare Natural Language Processing Question Answering

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Applications of AI in Preservation of Kumaoni Culture Through Digital Archival 3

Applications of AI in Preservation of Kumaoni Culture Throug...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Dev, Vaishali Dhingra, Ansh Sharma, Shashi Kumar Koturiya, Nishant Graphic Era Hill University Dept. of Computer Science and Engineering Bhimtal India

ISBN: (纸本)9798331519582

Culture represents a society with human values and ethics. Without civilization there can be no culture. Thus it is very important to preserve our culture as it represents our values and ethics. Each region has its own characteristics culture which separates it from other regions not just geographically but in terms of traditions, art and literature. The Kumaon region in the Indian state of Uttarakhand lies in the eastern part of the state. It is blessed with a rich culture heritage and nature. The preservation of culture is done through traditional methods. The focus of this study is to use modern Artificial Intelligence techniques to achieve the preservation of culture. The Artificial Intelligence techniques which are used in this paper range from Convolutional Neural Networks, augmentation of data and fine tuning of models. To preserve it is necessary to archive the cultural elements so that it can be easily accessible in the future. The archival process can help in cataloguing the cultural items. By culture we mean traditional arts, such as Aipan, local literature texts and historical structures. The archival process seamlessly integrates the Artificial Intelligence methods to effectively preserve the cultural elements. This process can be further enhanced by fine tuning the Convolutional Neural Networks. This makes the model more robust and accurate than the existing model. The dataset quality can be further improved by using data augmentation techniques. This allows to generate more data from existing dataset. However, with the process comes some challenges like accountability and validity. This study explores these challenges which can help existing researchers in the field of culture and arts. © 2025 IEEE.

关键词： Convolutional neural networks

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Blockchain Based E-Voting System Hosted on Cloud 3

Blockchain Based E-Voting System Hosted on Cloud

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Dalvi, Prajwal Prakash Mehare, Jayant Zade, Amol Gaikwad, Amit G H Raisoni University Dept. of Computer Science & Engineering Amravati India

ISBN: (纸本)9798331519582

Blockchain technology is becoming a great innovation at today's date due to its transparency, security and reliability. With the advancement in every aspect of life blockchain with its decentralized and distributed ledger system aims to provide immutability in digital transaction across the globe with the help of block structure joined together that stored the data in the form of hash. The data stored in block is next to immutable as if tried to change in a single block the hash of every block in the chain will change which makes it easy to identify. Due to its security and transparency the study describes its use in the e-voting system revolutionizing voting system with more security and transparency reducing logistic problem, solving storage issues, solves the problem of using extra man power in the large democracy like India. © 2025 IEEE.

关键词： Cloud computing

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Forecast-based Optimal Operation of EV Charging Station with PV Considering Charging Demand and Distributed System

Forecast-based Optimal Operation of EV Charging Station with...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Rho, Seoeun Chae, Myeongseok Won, Dongjun Inha University Dept. of Electrical and Computer Engineering Incheon Korea Republic of

ISBN: (纸本)9798350313604

In this paper, based on forecasting short-Term PV generation, demand, and EV charging, we propose a forecast-based optimal V2G scheduling strategy of Energy management system for minimizing the operating cost of an EV charging station and increasing the use of renewable energy. The significance of the developed method is constructing the detailed forecast model consisting of PV generation, regional load demand, and EV charging demand and analyzing the effectiveness of the scheduling participation rate in the case study. Demand types of office and residential areas were considered to establish a realistic EV charging station operation scheme, and short-Term electricity demand forecasts were made based on EV charging data. By integrating these various data, efficient power management is possible. The forecast-based scheduling results show that PV self-consumption has increased, and the main grid dependence and operating cost have decreased. © 2024 IEEE.

关键词： Forecasting

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Enhancing Mortality Prediction in ICU: A Deep Learning Framework for Multivariate Time Series Classification

Enhancing Mortality Prediction in ICU: A Deep Learning Frame...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Sathia Bhama, Ponsy R K Sarah R, Roshina Shobia, T.R. Vinothini, R. Anna University Dept. of Computer Technology MIT Campus Chennai India Sri Sairam Institute of Technology Dept. of Computer and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Relying on human diagnosis is potentially prone to errors, time-consuming and leading to treatment mistakes. Early Intensive Care Unit (ICU) mortality prediction is important for determining which patients are most likely to die and for offering appropriate interventions that will preserve their lives. However, conventional diagnostic approaches often face challenges in fully harnessing the extensive information contained within Electronic Health Record (EHR) data, particularly in capturing the intricate temporal dynamics of patient health. In this research paper, a hybrid model leverages the power of Temporal Convolutional Networks (TCN) in conjunction with Bidirectional Gated Recurrent Units (BiGRU) to effectively capture the complex temporal dependencies present in ICU time series data, before constructing the model, the data is pre-processed by cutting-edge Generative Adversarial Network (GAN)-based imputation techniques to handle missing values within the EHR data. Through this comprehensive framework, the goal is to significantly enhance mortality prediction accuracy, empower clinicians with actionable insights into patient prognosis, and ultimately improve patient outcomes in critical care settings. © 2024 IEEE.

关键词： Electronic health record

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SOH Estimation of Batteries using Lithium-Ion Internal Parameters with Convolution Neural Network and Gated Recurrent

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Transactions of the Korean Institute of Electrical Engineers 2023年第3期72卷 387-394页

作者： Park, Hyun-Yong Lim, Hee-Sung Lee, Kyo-Beum Dept. of Electrical and Computer Engineering Ajou University Korea Republic of Dept. of IT Convergence Engineering Ajou University Korea Republic of SK Signet Inc Korea Republic of

This paper proposes an estimation method for Lithium-Ion Batteries SOH by learning the batteries’ internal parameters using the Convolution Neural Network and the Gated Recurrent Unit. Various equivalent circuit models exist to represent the batteries’ internal parameters. Among these equivalent circuit models, the most representative model is the Randles model, and the data measured based on the Randles model is used as learning input data. The internal parameters of batteries change non-linearly depending on the operation condition and use time. So, nonlinear features are extracted using the CNN input as the batteries' parameters. The extracted features are used as an input of the GRU to learn the characteristics of change over time, and SOH is predicted through this. The learning dataset utilizes 17IND10 LibForSecUse of EMPIR, which validates the performance of the proposed model. Copyright © The Korean Institute of Electrical Engineers.

关键词： Lithium-ion batteries

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Hybrid RNN-CNN model for predicting stock market trends 1

Hybrid RNN-CNN model for predicting stock market trends

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Bhanujyothi, H.C. Jacob, I Jeena GITAM Deemed to be University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356816

Stock market prediction has become a challenging task in today's world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model. © 2024 IEEE.

关键词： Convolutional neural networks

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